通过端到端策略和乐观流畅的虚拟游戏来掌握策略纸牌游戏（代码和魔法传奇）
Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play
摘要：
深度强化学习结合Fictionary Play在许多基准测试游戏上显示出令人印象深刻的结果，然而，其中大多数都是单阶段的。相比之下，现实世界的决策问题可能由多个阶段组成，其中观察空间和行动空间在各个阶段之间可能完全不同。我们研究了一个两阶段的策略纸牌游戏Legends of Code and Magic，并提出了一个端到端的策略来解决多阶段游戏中出现的困难。我们还提出了一个乐观光滑虚拟博弈算法来寻找两人博弈的纳什均衡。我们的方法赢得了COG2022比赛的双重冠军。大量的研究验证并显示了我们方法的先进性。
英文摘要：
Deep Reinforcement Learning combined with Fictitious Play shows impressive results on many benchmark games, most of which are, however, single-stage. In contrast, real-world decision making problems may consist of multiple stages, where the observation spaces and the action spaces can be completely different across stages. We study a two-stage strategy card game Legends of Code and Magic and propose an end-to-end policy to address the difficulties that arise in multi-stage game. We also propose an optimistic smooth fictitious play algorithm to find the Nash Equilibrium for the two-player game. Our approach wins double championships of COG2022 competition. Extensive studies verify and show the advancement of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
